Sequential labeling and classification of data has many applications, including those in natural language processing and speech processing. Some example applications include search query tagging, advertisement segmentation, and language identification/verification.
Conditional random fields (CRFs) are discriminative models that directly estimate the probabilities of a state sequence conditioned on a whole observation sequence. For example, frames of audio signal data may be converted to features, with the state sequence predicted on all the frames. Note that this is in contrast to generative models such as the hidden Markov models (HMMs) that describe the joint probability of the observation and the states.
Because of their discriminative nature, and also because they are very flexible in choosing classification features, conditional random fields have been widely and successfully used to solve sequential labeling problems. One well-known type of conditional random field is the linear-chain conditional random field, which is commonly used due to its simplicity and efficiency. While acceptable performance is obtained by using linear chain conditional random fields, there are limitations associated with them. For example, such a conditional random field typically requires manual construction of the many different features that are needed to achieve good performance, as they lack the ability to automatically generate robust discriminative internal features from raw features.